Search Results (854)

Phenology is a key factor influencing the accuracy of regional-scale winter wheat-yield estimation. This study proposes a yield-estimation modeling framework centered on phenological zoning. Based on the remote sensing monitoring results of the heading stage of winter wheat in the Huang-Huai-Hai region from 2016 to 2021, the KNN-Ward spatial constraint clustering method was adopted to divide the Huang-Huai-Hai region into four consecutive wheat phenological zones. The results indicate a consistent spatio-temporal gradient in the phenology of winter wheat across the Huang-Huai-Hai region, characterized by later development in the northern areas and earlier development in the southern areas. The median day of year (DOY) for the heading stage in each zone varies by approximately 4 to 5 days, demonstrating a high degree of interannual stability. Building upon the phenological zoning outcomes, a multi-source data-driven random forest model was developed for wheat-yield estimation by integrating remote sensing data and meteorological variables during the wheat grain filling stage. This model incorporates remote sensing vegetation indices, crop growth parameters, and climatic factors as key input variables. Results show that the phenological zoning strategy significantly improves model prediction performance. Compared with the non-zoning model (R² = 0.46, RRMSE = 13.02%), the phenological zone model shows strong performance under leave-one-year-out cross-validation, with R² ranging from 0.54 to 0.68 and RRMSE below 12.50%. The phenological zoning model also exhibits more uniform residuals and higher prediction stability than models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of phenology-based zoning for regional yield estimation and provide a reliable framework for fine-scale crop yield monitoring. The phenological zoning model also demonstrates superior residual uniformity and prediction stability compared with models based on non-zoning, traditional agricultural zoning, and provincial administrative zoning. These results confirm the effectiveness of the multi-factor-driven modeling framework based on crop phenological zoning for regional yield estimation, providing a robust methodological foundation for fine-scale yield monitoring at the regional level. Full article

Unmanned aerial vehicles (UAVs) with multispectral sensors are transforming precision viticulture by enabling detailed monitoring of vineyard variability. Vegetation indices such as NDVI, NDRE, GNDVI, and SAVI are widely applied to estimate vine vigor, canopy structure, and water status. Beyond agronomic traits, UAV-derived indices can inform grape composition, including sugar content (°Brix), total phenolics, anthocyanins, titratable acidity, berry weight, and yield variables measurable in the field or laboratory to validate spectral predictions. Strengths of UAV approaches include high spatial resolution, rapid data acquisition, and flexibility across vineyard blocks, while limitations involve index saturation in dense canopies (e.g., Merlot, Cabernet Sauvignon), environmental sensitivity, and calibration requirements across varieties and phenological cycles. Integrating UAV data with ground-based measurements (leaf sampling, yield mapping, proximal or thermal sensors) improves model accuracy and stress detection. Abiotic stresses (water deficit, nutrient deficiency) can be distinguished from biotic factors (pest and fungal infections), supporting timely interventions. Compared to manned aircraft or satellite platforms, UAVs offer cost-effective, high-resolution imagery for precision vineyard management. Future directions include combining UAV indices with machine learning and data fusion to predict grape maturity and wine quality, enhancing decision-making in sustainable viticulture and precision enology. Full article

Understanding the reproductive biology of orchids is essential for evaluating population viability and guiding conservation strategies, as their persistence often depends on complex interactions between ecological, physiological, and environmental factors. Govenia capitata, a threatened orchid endemic to the montane forests of central Mexico, had not previously been studied in this regard. We examined flowering phenology, floral longevity, stigmatic receptivity, natural and experimental pollination success, seed viability, and asymbiotic germination in two wild populations. Flowering was synchronous, with inflorescences lasting up to 57 days and individual flowers persisting for an average of 20 days. Stigmatic receptivity was detectable from the first day of anthesis and remained evident for at least eight days. Natural fruit set was very low (16.6%), while assisted self- and cross-pollination reached 100% success, demonstrating self-compatibility despite the inability for autonomous selfing due to floral structure. Seed viability differed significantly among treatments, being lowest in selfed capsules (11%) and highest in cross-pollinated ones (32%), representing a 65% reduction and reflecting severe inbreeding depression that extended to germination performance. In vitro germination success also varied, with the L-arginine medium yielding the highest values (46% for cross-pollinated seeds and 44% for naturally pollinated seeds), though post-germination survival requires optimization for conservation applications. Despite the conspicuous floral display, floral visitation was extremely rare and the pollinator identity remains unknown, with only one potentially effective visitor observed during 144 h of monitoring, and most floral visitors were non-pollinating arthropods such as crab spiders, weevils, hymenopterans, and thrips. Population density varied dramatically (26-fold) between sites separated by less than 1 km, indicating pronounced sensitivity to local environmental conditions. These findings reveal that reproduction in G. capitata is constrained by both extrinsic (pollinator limitation) and intrinsic factors (reduced seed viability), which collectively jeopardize long-term population persistence. From a conservation perspective, protecting montane forest remnants and pollinator communities is essential, while the demonstrated potential of asymbiotic germination provides a complementary tool for ex situ propagation and management of this endemic orchid. Full article

Amphibian breeding phenology provides key insights into species’ sensitivity to climatic and anthropogenic drivers. We used passive acoustic monitoring (PAM) with automated call detection to examine the calling activity of Dryophytes japonicus and Pelophylax nigromaculatus in South Korean rice paddies across five breeding seasons (2018–2022). Both species exhibited distinct seasonal patterns: D. japonicus showed a synchronous and concentrated calling peak in mid-June (GAM deviance explained = 34%), whereas P. nigromaculatus initiated calling earlier and maintained a longer, less synchronized calling period extending into July (GAM deviance explained = 19%). Zero-inflated negative binomial models demonstrated that temperature was the strongest predictor of calling activity in both species, though responses to humidity and wind differed. D. japonicus maintained high calling rate under warm conditions, with only modest suppression at high humidity, whereas P. nigromaculatus was strongly inhibited by combined warm and humid conditions. These results establish a detailed information on the calling phenology of D. japonicus and P. nigromaculatus in East Asian agroecosystems highlight species-specific sensitivities to local weather variables. Our findings demonstrate that automated acoustic monitoring offers an efficient way to document ecological responses to weather variability and may serve as a long-term tool to track phenological shifts under climate change. Future advances in sound analysis, including the integration of deep-learning algorithms and cross-species detection frameworks, could further improve automated biodiversity monitoring in complex agricultural landscapes. Full article

Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM, a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board, developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana) with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management. Full article

Climate change poses significant challenges to viticulture worldwide, with Eastern European vineyards experiencing increased water stress due to rising temperatures, irregular precipitation patterns, and prolonged drought periods. These climatic shifts hurt vine phenology, grape quality, and overall productivity. In response, adaptive irrigation strategies such as Regulated Deficit Irrigation (RDI) have gained attention for optimizing water use while preserving grape quality. Concurrently, the adoption of smart agriculture technologies—including soil moisture sensors, automated weather stations, remote sensing, and data-driven decision support systems—enables precise monitoring and real-time management of vineyard water status. This review synthesizes recent studies from Eastern Europe, emphasizing the necessity of integrating climate adaptation measures with intelligent irrigation management to enhance vineyard resilience and sustainability under increasing climate variability. Full article

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

Insects can respond rapidly to climate change through population fluctuations, range shifts, altered voltinism, life cycle changes, flight period adjustments, behavioural shifts, and changes in habitat or food preference—often varying by region due to local environmental and anthropogenic factors. While the phenological cycles of diurnal lepidopterans have been extensively studied in countries with large monitoring networks, eastern and southeastern Europe remain under-researched. This study provides the first insights into phenological shifts in 16 butterfly species in Cluj-Napoca (Transylvania, Romania) between 1921 and 2023, using a unique dataset combining historical and recent records. The species studied include spring-emerging, multivoltine, and migratory butterflies. Phenological trends were analyzed in relation to long-term climatic data. Results show that spring species now emerge approximately 15 days earlier, and autumn species extend their flight periods by up to 23 days. These changes correlate with multi-decadal trends in temperature and precipitation. We also discuss changes in voltinism and migratory behaviour and the potential impacts of climate change on butterfly populations in the study region. Full article

The highbush blueberry (Vaccinium corymbosum L.) is a promising fruit species due to its high nutritional value and health benefits. This study, conducted between 2020 and 2024, monitored the phenological and pomological characteristics of 32 different blueberry cultivars grown in the Czech Republic. The evaluation was carried out according to Czech Republic standardized methodologies, BBCH (Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie) and GRIN (Genetic Resources Information Network), and included parameters such as fruit size, flavor, aroma, firmness, color, and soluble solids content (SSC in °Brix). The correlation between individual traits was assessed, along with their phenotypic stability. The results showed that all cultivars exhibited high pomological values, making them suitable for breeding programs. The cultivars ‘Collins’ and ‘Patriot’ received the highest flavor ratings. Firmness, aroma, and color traits were found to be correlated with consumer preferences. The interannual coefficient of variation (CV) obtained for the evaluated blueberry cultivars differed for both pomological and phenological traits, allowing the identification of genotypes with high stability (CV ≤ 10%) and their potential use in targeted breeding programs and industrial production. The ‘Pink Lemonade’ blueberry cultivar, in particular, combines unique color characteristics with a strong aroma. Overall, this study provides valuable insights for future breeding efforts aimed at improving blueberry quality and cultivar adaptability under different cultivation conditions. Full article

Vegetation phenology (VP) is a crucial biological indicator for monitoring terrestrial ecosystems and global climate change. However, VP monitoring using traditional remote sensing vegetation indices has significant limitations in precise analysis. Furthermore, most studies have overlooked the distinction between stable and short-term VP in relation to climate change and have failed to clearly identify the seasonal variation in the impact of climatic factors on stable VP (SVP). This study compared the accuracy of solar-induced chlorophyll fluorescence (SIF) and three traditional vegetation indices (e.g., Normalized Difference Vegetation Index) for estimating SVP in China, using ground-based data for validation. Additionally, this study employs Sen’s slope, the Mann–Kendall (MK) test, and the Hurst index to reveal the spatiotemporal evolution of the Start of Season (SOS), End of Season (EOS), and Length of Growing Season (LOS) over the past two decades. Partial correlation analysis and random forest importance evaluation are used to accurately identify the key climatic drivers of SVP across different climate zones and to assess the seasonal contributions of climate to SVP. The results indicate that (1) phenological metrics derived from SIF data showed the strongest correlation coefficients with ground-based observations, with all correlation coefficients (R) exceeding 0.69 and an average of 0.75. (2) The spatial distribution of SVP in China has revealed three primary spatial patterns: the Tibetan Plateau, and regions north and south of the Qinling–Huaihe Line. From arid, cold-to-warm, and humid regions, the rate of SOS advancement gradually increases; EOS transitions from earlier to nearly unchanged; and the rate of LOS delay increases accordingly. (3) The spring climate primarily drives the advancement of SOS across China, contributing up to 70%, with temperatures generally having a negative effect on SOS (r = −0.53, p < 0.05). In contrast, EOS is regulated and more complex, with the vapor pressure deficit exerting a dual ‘limitation–promotion’ effect in autumn (r = −0.39, p < 0.05) and summer (r = 0.77, p < 0.05). This study contributes to a deeper scientific understanding of the interannual variability in SVP under seasonal climate change. Full article

Reliable and representative ground data is fundamental for accurate crop classification using satellite imagery. This study demonstrates a structured approach to ground truth planning in the Bareilly district, Uttar Pradesh, where wheat is the dominant crop. Pre-season spectral clustering of Sentinel-2 Level-2A NDVI time-series data (November–March) was applied to identify ten spectrally distinct zones across the district, capturing phenological and land cover variability. These clusters were used at the village level to guide spatially stratified and optimized field sampling, ensuring coverage of heterogeneous and agriculturally significant areas. A total of 197 ground truth points were collected using the iCrops mobile application, enabling standardized and photo-validated data collection with offline functionality. The collected ground observations formed the basis for random forest supervised classification, enabling clear differentiation between major land use and land cover (LULC) classes with an overall accuracy of 91.6% and a Kappa coefficient of 0.886. The findings highlight that systematic ground data collection significantly enhances the reliability of remote sensing-based crop mapping. The outputs serve as a valuable resource for agricultural planners, policymakers, and local stakeholders by supporting crop monitoring, land use planning, and informed decision-making in the context of sustainable agricultural development. Full article

The Mediterranean fig (Ficus carica L.) is a dioecious fruit tree of high nutritional and economic value in the Mediterranean basin. In northern Morocco, phenological desynchronization between male and female fig trees limits pollination and production. This study aimed to characterize the phenological stages of indigenous fig and caprifig varieties using the BBCH scale and to evaluate the predictive capacity of artificial neural networks (ANNs). This study was conducted in the Bni Ahmed region over two consecutive years (2021 and 2022) at two sites. At each site, a total of 80 female fig trees were selected. Caprifig trees were selected in accordance with their availability (37 trees/site 1; 24 trees/site 2). Local meteorological data were incorporated into the analysis to evaluate the influence of climatic conditions on phenological stages. Our results revealed significant effects of temperature, humidity, and rainfall on phenological dynamics, along with a clear inter-varietal variability and pronounced desynchronization between male and female fig trees. Early-ripening caprifig varieties showed limited pollination efficiency, whereas late-ripening varieties were better synchronized with the longer receptivity period of female fig trees. Importantly, the ANN model demonstrated exceptional predictive performance (R² up to 0.985, RMSE < 1 day), serving as a robust and practical tool for forecasting key phenological stages and minimizing potential yield losses. These findings demonstrate the value of combining phenological monitoring with AI-based modeling to improve adaptive management of fig orchards under Mediterranean climate change. This is the first study in Morocco to implement such an integrated approach to fig and caprifig trees. Full article

Xylella fastidiosa was first detected in Portugal in 2019 in Lavandula dentata. In response, the national plant health authorities promptly established a Demarcated Zone in the affected area and implemented a series of eradication and control measures, including the systematic removal and destruction of infected and host plants. This study analyzes the economic and operational impacts of these eradication efforts in the northern region of Portugal, with a focus on Demarcated Zones such as the Porto Metropolitan Area, Sabrosa, Alijó, Baião, Mirandela, Mirandela II, and Bougado between 2019 and June 2023. During this period, about 412,500 plants were uprooted. The majority were Pteridium aquilinum (bracken fern), with 360,324 individuals (87.3%), reflecting its wide distribution and the large area affected. Olea europaea (olive tree) was the second most common species removed, with 7024 plants (1.7%), highlighting its economic relevance. Other notable species included Quercus robur (3511; 0.85%), Pelargonium graveolens (3509; 0.85%), and Rosa spp. (1106; 0.27%). Overall, destruction costs were estimated at about EUR 1.04 million, with replanting costs of roughly EUR 6.81 million. In parallel, prospection activities—conducted to detect early signs of infection and monitor disease spread—generated expenses of roughly EUR 5.94 million. While prospecting represents a significant financial investment, the results show that it is considerably more cost-effective than large-scale eradication. Prospection enables early detection and containment, preventing the widespread destruction of vegetation and minimizing disruption to agricultural production, biodiversity, and local communities. Importantly, our findings reveal a sharp decline in confirmed cases in the initial outbreak area—the Porto Demarcated Zone—from 124 cases in 2019 to just 5 in 2023, indicating the effectiveness of the eradication and monitoring measures implemented. However, the presence of 20 active Demarcated Zones across the country as of 2023 highlights the continued risk of spread and the need for sustained vigilance. The complexity of managing Xylella fastidiosa across ecologically and logistically diverse territories justifies the high costs associated with surveillance and targeted interventions. This study reinforces the strategic value of prospection as a proactive and sustainable tool for plant health management. Effective surveillance requires the integration of advanced methodologies aligned with the phenological stages of host plants and the biological cycle of vectors. Targeting high-risk locations, optimizing sample numbers, ensuring diagnostic accuracy, and maintaining continuous training for field teams are critical for improving efficiency and reducing costs. Ultimately, the findings underscore the need to refine and adapt monitoring and eradication strategies to contain the pathogen, safeguard agricultural systems, and prevent Xylella fastidiosa from becoming endemic in Portugal. Full article

The aim of this study is to determine the reliability of regular and spatial cross-validation methods in predicting subfield-scale maize yields using phenological measures derived by Sentinel-2. Three maize fields from eastern Croatia were monitored during the 2023 growing season, with high-resolution ground truth yield data collected using combine harvester sensors. Sentinel-2 time series were used to compute two vegetation indices, Enhanced Vegetation Index (EVI) and Wide Dynamic Range Vegetation Index (WDRVI). These features served as inputs for three machine learning models, including Random Forest (RF) and Bayesian Generalized Linear Model (BGLM), which were trained and evaluated using both regular and spatial 10-fold cross-validation. Results showed that spatial cross-validation produced a more realistic and conservative estimate of the performance of the model, while regular cross-validation overestimated predictive accuracy systematically because of spatial dependence among the samples. EVI-based models were more reliable than WDRVI, generating more accurate phenomenological fits and yield predictions across parcels. These results emphasize the importance of spatially explicit validation for subfield yield modeling and suggest that overlooking spatial structure can lead to misleading conclusions about model accuracy and generalizability. Full article

A self-sustaining population of birdfoot violet (Viola pedata), a host plant for the threatened western subspecies of regal fritillary butterfly (Argynnis idalia occidentalis) caterpillar, was examined during a single year from April to June 2021 on a small, 3.1 ha dry bluff prairie hillslope within the Whitewater Wildlife Management Area in southeastern Minnesota USA. Assessments were conducted to determine if violet populations on small prairie remnants could support seed collecting to establish new populations nearby. Ten transects and five random plots were used to assess violet density and monitor violet growth, reproductive phenology, and seed production. Violet densities were high (>5 plants/m²), with greatest densities at middle elevations on the hillside in the middle of the prairie rather than near the edges. The total population of birdfoot violets on the hillside was extrapolated from density estimates based on 200, 1-m² plots to be >62,000 plants. Seed set was low (less than one pod per plant) but nearly 400,000 total seeds were produced during the 2021 growing season. More than 3000 seeds (<1% of estimated seed production on the study hillslope) were collected for out-planting to establish a new violet population in nearby Whitewater State Park. Some small bluff prairies in southeastern Minnesota and elsewhere under certain conditions may sustain violet populations large enough to permit seed collecting to establish additional populations during restoration of native prairie communities. These ultimately should provide much needed habitat for regal fritillary butterflies to partially compensate for ongoing habitat losses. Full article